I am a fifth year PhD student in Computer Science at Harvard University where I am advised by Yaron Singer.
My research is at the intersection of machine learning and algorithms. My most recent line of work on adaptivity develops parallel algorithms for machine learning applications. For a broad class of optimization problems, these new algorithms obtain exponential speedups in parallel runtime. I am also interested in the closely related area of optimization from samples, where sampled data and machine learning are used for decision tasks, instead of prediction tasks. For more details, my publications can be found here.
I am supported by a Google PhD Fellowship.
I interned at Google research NYC in the modeling and data science group in 2016 and in the algorithms group in 2017. Before my PhD, I was an undergraduate student at Carnegie Mellon University where I graduated in 2014.
Program Committees: NIPS 2016, KDD 2017, NIPS 2017 (best reviewer award), ICML 2018, NIPS 2018